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In this study, Imani et al. investigate whether a new proprietary herbal formulation, HF1, can inhibit expression of immune suppressor protein PD-L1. PD-L1 is a transmembrane protein that can be expressed by cancer cells to assist in their ability to avoid attacks from the immune system. Work from this study demonstrates that HF1 treatment can reduce expression of PD-L1 in cultured cancer cells, implicating HF1 as a potential new cancer therapy.

In​ the​ field​ of​ medicine,​ natural​ treatments​ are​ becoming ​increasingly ​vital ​towards ​the ​cure ​of ​cancer. Zhu et al. wanted to investigate the effects of lettuce extract on cancer cell survival and proliferation. They used an adenocarcinoma cell line, COLO320DM, to determine whether crude extract from a lettuce species called Niuli​ Lactucis Agrestibus​ would affect cancer cell survival, migration, and proliferation. They found that Niuli extract inhibited cancer cell survival, increased expression of cell cycle inhibitors p21 and p27, and inhibited migration. However, Niuli extract did not have these effects on healthy cells. This work reveals important findings about a potential new source of anti-colorectal cancer compounds.

Some cancer treatments lose efficacy when combined with treatments for excessive stomach acid, due to the changes in the stomach environment caused by the stomach acid treatments. Lin and Lin investigate information on oral cancer drugs to see what information is available on interactions of these drugs.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with early diagnosis and treatment challenges. When any of the genes KRAS, SMAD4, TP53, and BRCA2 are heavily mutated, they correlate with PDAC progression. Cellular stress, partly regulated by the gene SERPINA6, also correlates with PDAC progression. When SERPINA6 is highly expressed, corticosteroid-binding globulin inhibits the effect of the stress hormone cortisol. In this study, the authors explored whether there is an inverse correlation between the expression of SERPINA6 and PDAC-linked genes.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

The authors assess incidence and mortality rates of two cancer types in relation to trihalomethane pollutant concentrations in drinking water.

This study investigates the effects of the PROTAC compound A1874 on CT26 colon carcinoma cells, focusing on its ability to degrade the protein BRD4 and reduce cell viability. While A1874 had previously shown effectiveness in other colon cancer cell lines, its impact on CT26 cells was unknown.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

Current osteosarcoma (OS) treatments rely on surgery and chemotherapy, but drug resistance remains a major challenge that lowers patient survival rates. Ferroptosis, a form of regulated cell death, has shown promise in cancer therapy but is not well understood in OS. This study explores the use of Ferroptosis in OS.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.